In this proposal we develop and implement new methods for detecting genetic factors that influence the expression of quantitative traits such as hypertension and body mass index. We further develop methods of analysis for identifying genetic factors by genetic linkage analysis and through association studies. Our first aim is to implement multivariate, multipoint variance components procedures for complex traits on multiple platforms, including PCs, workstations and mainframe computers. This aim builds upon the resources that we have developed to provide more general access for researchers to our Analysis of Complex Traits (ACT) package. ACT is available through the World Wide Web to the statistical genetic community for use on workstations using UNIX operating systems. The package performs multipoint, multivariate linkage analysis of traits. We propose to modify the programs so that they can be run in a personal computing environment, and to assist the user by implementing graphical user interfaces. For this aim, our graphical interfaces are primarily directed at improving access to online help for the documentation and the interfaces would assist the user choosing analyses to be run. We also propose some more minor modifications to our programs that would permit broader classes of genetic models to be fitted. Our second aim is to develop new statistical methods and statistical software that incorporate linkage disequilibrium in the analysis of quantitative traits. Our group of investigators have developed methods for performing family-based association tests for quantitative traits. We propose to develop software to help investigators to use these family-based tests. Our third aim is to develop new multivariate methods for genetic analysis that incorporate linkage disequilibrium. We also propose methods to assist researchers in designing studies of complex traits. Our fourth aim is to develop software for automated sample size and cost estimation. We routinely assess the power of all new tests that we develop. In this grant we will devote resources to developing a user-friendly set of programs that will include a graphical user interface to assist people in the design of studies of complex traits. Our last aim is to develop new methods for meta-analysis using linkage disequilibrium methods for quantitative traits. Power of any separate study to identify genetic factors influencing a trait may be so low, so we propose to develop methods to efficiently pool results from multiple studies, allowing for the potential genetic and methodologic heterogeneity among studies.